analiza aiqbench
This paper applies a generative model to RNA structure: a sequence-conditioned network that produces ensembles of backbone conformations, using experimental B-factors to set per-residue noise widths during training. The reported gains are on a multi-conformer benchmark, with ablations showing better recovery under stronger perturbations, but the evidence is limited to that benchmark and a preprint. Readers interested in ML for structural biology should watch whether the calibration approach holds up on other RNA datasets.
REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge
arXiv:2610.26261v1 Announce Type: new Abstract: RNA molecules often function through multiple experimentally observed conformational states. A central challenge is to capture ensemble diversity without sacrificing structural fidelity: samples may collapse toward averaged representative structures or drift into unsupported conformational regions. We introduce REFLEX (RNA Ensemble generation calibrated by FLEXibility), a sequence-conditioned generator for RNA backbone-frame ensembles based on a heteroscedastic stochastic bridge on SE(3). During training, experimental B-factors calibrate residue-wise bridge widths, exposing each observed conformer through local perturbation neighborhoods with residue-specific scales while keeping constrained regions sharper. A learned flexibility condition guides the bridge-field network to recover target conformer-specific frames from these calibrated noisy states. At inference, REFLEX uses only the input sequence, requiring no MSA-subsampling heuristics. On a multi-conformer RNA benchmark, REFLEX achieves the best precision-recall trade-off for ensemble coverage across evaluation thresholds, while recovery-stress ablations indicate gains in target-frame recovery under stronger perturbations.
Abstract:RNA molecules often function through multiple experimentally observed conformational states. A central challenge is to capture ensemble diversity without sacrificing structural fidelity: samples may collapse toward averaged representative structures or drift into unsupported conformational regions. We introduce REFLEX (RNA Ensemble generation calibrated by FLEXibility), a sequence-conditioned generator for RNA backbone-frame ensembles based on a heteroscedastic stochastic bridge on SE(3). During training, experimental B-factors calibrate residue-wise bridge widths, exposing each observed conformer through local perturbation neighborhoods with residue-specific scales while keeping constrained regions sharper. A learned flexibility condition guides the bridge-field network to recover target conformer-specific frames from these calibrated noisy states. At inference, REFLEX uses only the input sequence, requiring no MSA-subsampling heuristics. On a multi-conformer RNA benchmark, REFLEX achieves the best precision-recall trade-off for ensemble coverage across evaluation thresholds, while recovery-stress ablations indicate gains in target-frame recovery under stronger perturbations.
Artificial Intelligence (cs.AI)
arXiv:2610.26261 [cs.AI]
(or arXiv:2610.26261v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26261 Focus to learn more arXiv-issued DOI via DataCite
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